Data Support Sr. Analyst

Job Description:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day. Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits. We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve. Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs. At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

Background:

Global Risk Management (GRM) leads bank-wide initiatives for the management of strategic, market, credit, compliance, liquidity, operational, model and reputational risk. In a data-driven economy, trusted and well-governed data is foundational to sustainable risk management and enterprise value. Within GRM, the Data Strategy & Management function is strengthening the data strategy, governance and related AI capabilities needed to support risk lines of business through reusable solutions, modern data architecture and effective governance.

Job Description:

Under the GRM Data Strategy & Management leadership team, the Data Analyst / AI Engineer will design, build and operationalize quantitative models and agentic AI solutions for complex risk and data-management use cases. This is a hands-on role for an experienced practitioner who can move from problem framing and data discovery through experimentation, production deployment, monitoring and continuous improvement. The role will partner with risk subject-matter experts, data owners, technology teams and governance partners to deliver explainable, scalable and controlled solutions in a highly regulated environment.

The successful candidate will combine strong Python and data engineering skills with basic understanding of data strategy, data lineage, metadata, data quality and data governance. The candidate should be able to translate business and control objectives into production-grade data and AI products, while documenting data provenance, model behavior, decisions and human oversight.

Responsibilities:

• Build agentic AI solutions that use large language models, retrieval-augmented generation, tools, APIs and workflow orchestration to support research, analysis, decision support and operational processes.
• Develop robust Python services, reusable libraries, data pipelines and APIs that integrate structured and unstructured data from multiple Systems of Record.
• Create evaluation frameworks for ML and generative AI solutions, including accuracy, precision/recall, robustness, latency, cost, hallucination, grounded-ness and task-completion measures.
• Implement appropriate human-in-the-loop controls, guardrails, audit trails, access controls, fallback paths and approval checkpoints for agentic workflows.
• Partner with business and risk stakeholders to frame problems, define measurable success criteria and translate prototypes into scalable, production-grade capabilities.
• Apply data strategy principles to identify authoritative sources, promote reuse, minimize redundant sourcing and align solutions to target-state data architecture.
• Document and maintain end-to-end data lineage from source systems through transformations, features, models, prompts, agent actions and downstream outputs.
• Embed data governance requirements into solution design, including metadata, ownership, classifications, quality controls, retention, privacy, entitlements and issue management.
• Perform data discovery, profiling, quality assessment and root-cause analysis across large, complex datasets; establish controls and metrics to monitor data health.
• Collaborate in cross-functional Agile delivery teams and communicate technical concepts, trade-offs, risks and outcomes clearly to both technical and executive audiences.
• Mentor team members, contribute reusable patterns and standards, and help advance engineering discipline across GRM data and AI initiatives.

Required Skills:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Engineering, Statistics, Mathematics or a related quantitative discipline.
• 5+ years of professional experience building data analytics, machine learning or AI solutions, with demonstrated ownership of solutions beyond experimentation and into production.
• Advanced hands-on Python development, including clean, testable and modular code; experience with pandas, NumPy, scikit-learn and at least one deep-learning framework such as PyTorch or TensorFlow.
• Practical knowledge of supervised and unsupervised learning, model selection, benchmarking, error analysis and explain ability.
• Hands-on experience building and deploying generative AI or agentic AI solutions using LLMs, retrieval-augmented generation, embeddings, vector search, tool calling, prompt management, multi-step orchestration using LangGraph and MCP
• Experience designing production data/ML pipelines and services using technologies such as SQL, Spark, Airflow, Databricks, REST APIs, Git, Docker and CI/CD.
• Fluency in data strategy, including authoritative-source selection, data-product thinking, target-state architecture, reuse, interoperability and business-value alignment.
• Strong working knowledge of data lineage and metadata management, including the ability to trace data from origin through transformations and consumption, identify controls and document dependencies.
• Strong working knowledge of data governance, including data ownership, classification, quality, privacy, access, retention, controls and issue remediation.
• Exposure to modern data platforms and storage patterns such as Snowflake, Databricks, Delta Lake, Apache Iceberg, Parquet, JSON or Avro.
• Experience working with relational and NoSQL stores, large and heterogeneous datasets, and structured and unstructured information.
• Ability to operate effectively in a regulated environment with strong documentation, change management, validation, testing and audit-readiness expectations.
• Exceptional written, verbal and visual communication skills, with the ability to explain complex technical topics and influence stakeholders at multiple levels.

What Success Looks Like:

• Delivers measurable improvements in analyst capacity, decision quality, risk coverage, cycle time or control effectiveness.
• Moves viable use cases from prototype to sustainable model with clear ownership and controls
• Creates reusable data, ML and agentic AI components that can be adopted across GRM rather than built as one-off solutions.
• Improves transparency and trust through documented lineage, governance, explain ability and evidence-based performance metrics.

Skills:

• Attention to Detail
• Collaboration
• Data Governance
• Data Quality Management
• Risk Management
• Adaptability
• Controls Management
• Negotiation
• Problem Solving
• Result Orientation
• Change Management
• Continuous Improvement
• Data Management
• Presentation Skills
• Stakeholder Management

Shift:

Hours Per Week: